自然-启发增强的重复数据删除,用于高效的云存储

G. Madhubala, R. Priyadharshini, P. Ranjitham, S. Baskaran
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引用次数: 5

摘要

云计算是将计算作为一种服务交付,它特别涉及到数据的存储,从而实现无处不在、方便地访问共享资源,这些资源作为一种实用工具通过网络提供给计算机和其他设备。存储(被认为是关键属性)受到数据冗余副本存在的阻碍。重复数据删除是一种专门的数据压缩和重复检测技术,用于消除重复的数据副本,从而提高存储利用率。云服务提供商目前采用哈希技术,以避免冗余副本的存在。显然,有几个主要的陷阱可以通过采用自然启发的遗传编程方法来克服,以实现重复数据删除。遗传规划是利用生物进化的思想来处理复杂问题的一种系统的、领域独立的规划模型。采用序列匹配算法和Levenshtein算法进行文本比较,然后利用遗传规划的概念检测最接近的匹配。比较了这三种算法和散列技术的性能。由于生物启发的概念、系统和算法被发现更有效,因此在云存储中实施了一种自然启发的重复数据删除方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Nature - Inspired enhanced data deduplication for efficient cloud storage
Cloud Computing is the delivery of computing as a service, which is specifically involved with Storage of data, enabling ubiquitous, convenient access to shared resources that are provided to computers and other devices as a utility over a network. Storage, which is considered to be the key attribute, is hindered by the presence of redundant copies of data. Data Deduplication is a specialized technique for data compression and duplicate detection for eliminating duplicate copies of data to make storage utilization efficient. Cloud Service Providers currently employ Hashing technique so as to avoid the presence of redundant copies. Apparently, there are a few major pitfalls which can be vanquished through the employment of a Nature - Inspired, Genetic Programming Approach, for deduplication. Genetic Programming is a systematic, domain - independent programming model making use of the ideologies of biological evolution so as to handle a complicated problem. A Sequence Matching Algorithm and Levenshtein's Algorithm are used for Text Comparison and then Genetic Programming concepts are utilized to detect the closest match. The performance of these three algorithms and hashing technique are compared. Since bio-inspired concepts, systems and algorithms are found to be more efficient, a Nature-Inspired Approach for data deduplication in cloud storage is implemented.
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